Medark: a map-matching error detection and rectification framework for vehicle trajectories

Abstract

The widespread use of Global Navigation Satellite System (GNSS) trackers has significantly enhanced the availability of vehicle tracking data, providing researchers with critical insights into human mobility. Map matching, a key preprocessing step in movement analysis, matches vehicle tracking data to road segments but often introduces errors that can affect subsequent analyses. Existing map-matching methods, categorized into classic spatially generalizable methods and region-specific deep-learning-based methods, both have limitations. Region-specific deep learning methods, while more accurate, do not transfer well across different geographical regions. Moreover, the temporal adaptability of both approaches—their ability to handle GNSS signals of varying sampling intervals—has not been thoroughly examined. To overcome these limitations, we introduce Medark, a novel framework for detecting and rectifying errors in classic map-matching methods while preserving spatial generalizability. The proposed model is trained using a transfer-learning approach with synthetic trajectories generated in Ann Arbor and Los Angeles at various sampling intervals and a real vehicle trajectory dataset from Ann Arbor. Our experimental results validate the effectiveness of Medark. This framework can be integrated with any map-matching method to improve accuracy and produce high-quality trajectories for further analysis.